High Density Individually Addressable Nanowire Arrays Record Intracellular Activity from Primary Rodent and Human Stem Cell Derived Neurons.

We report a new hybrid integration scheme that offers for the first time a nanowire-on-lead approach, which enables independent electrical addressability, is scalable, and has superior spatial resolution in vertical nanowire arrays. The fabrication of these nanowire arrays is demonstrated to be scalable down to submicrometer site-to-site spacing and can be combined with standard integrated circuit fabrication technologies. We utilize these arrays to perform electrophysiological recordings from mouse and rat primary neurons and human induced pluripotent stem cell (hiPSC)-derived neurons, which revealed high signal-to-noise ratios and sensitivity to subthreshold postsynaptic potentials (PSPs). We measured electrical activity from rodent neurons from 8 days in vitro (DIV) to 14 DIV and from hiPSC-derived neurons at 6 weeks in vitro post culture with signal amplitudes up to 99 mV. Overall, our platform paves the way for longitudinal electrophysiological experiments on synaptic activity in human iPSC based disease models of neuronal networks, critical for understanding the mechanisms of neurological diseases and for developing drugs to treat them.

ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks.

Deep Quantization (below eight bits) can significantly reduce the DNN computation and storage by decreasing the bitwidth of network encodings. However, without arduous manual effort, this deep quantization can lead to significant accuracy loss, leaving it in a position of questionable utility. We propose a systematic approach to tackle this problem, by automating the process of discovering the bitwidths through an end-to-end deep reinforcement learning framework (ReLeQ). This framework utilizes the sample efficiency of proximal policy optimization to explore the exponentially large space of possible assignment of the bitwidths to the layers. We show how ReLeQ can balance speed and quality, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks with minimal accuracy loss (≤ 0.3% loss) while minimizing the computation and storage costs. With these DNNs, ReLeQ enables conventional hardware and custom DNN accelerator to achieve 2.2× speedup over 8-bit execution.